Hexagonal Binning
Chart overview
Hexagonal binning divides the 2D plane into a regular hexagonal grid and colours each cell by the number of data points it contains, creating a smooth density map that remains readable at very large sample sizes.
Key points
- Scientists in genomics, astrophysics, and imaging use it when standard scatter plots produce an uninformative black mass due to overplotting.
- The hexagonal geometry minimises binning artefacts compared to square grids and allows accurate perception of density gradients.
Python Tutorial
How to create a hexagonal binning in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
Complete Guide to Scientific Data VisualizationExample Visualization
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"Create a hexagonal binning plot from my data. Choose an appropriate grid size, colour hexagons by point count using a sequential colormap, add a colorbar labelled with count or density, overlay marginal histograms if space permits, and format as a publication-quality figure."
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Python Code Example
Console Output
Figure saved: plotivy-hexagonal-binning.png
Common Use Cases
- 1Visualising allele frequency distributions from genome-wide association studies with millions of SNPs
- 2Showing photometric magnitude versus colour index for stellar populations in large sky surveys
- 3Displaying the relationship between two biomarkers measured across thousands of patients
- 4Representing pixel intensity co-occurrence in multi-channel fluorescence microscopy data
Pro Tips
Adjust hexbin gridsize parameter to balance detail and smoothness for your sample size
Use a logarithmic colour scale when count values span several orders of magnitude
Overlay a contour line at key density percentiles to highlight the core data region
Remove empty hexagons by setting a minimum count threshold to reduce visual clutter
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for hexagonal-binning.
numpy
Useful in specialized workflows that complement core Python plotting libraries for hexagonal-binning analysis tasks.
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.